April 30, 2018

Overview of Work/Research

  • Segmentation/Classification of:
    • White Matter Lesions in Multiple Sclerosis
    • Brain vs. Skull (CT)
    • Brain Hemorrhage/Stroke (CT)
  • R Package Development/“Data Science”
  • Neuroimaging and R (Neuroconductor Project)

Overview of Work/Research

  • Segmentation/Classification of:
    • White Matter Lesions in Multiple Sclerosis
    • Brain vs. Skull (CT)
    • Brain Hemorrhage/Stroke (CT)
  • R Package Development
  • Neuroimaging and R (Neuroconductor Project)

Lesion Segmentation of MS

Public Dataset with Lesion Segmentation

  • “A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus” (Lesjak et al. 2018)

Demographic Data

  • On many different therapies (9 no therapy)
Variable Overall
n 30
Age (mean (sd)) 39.27 (10.12)
EDSS (mean (sd)) 2.61 (1.88)
Lesion_Volume (mean (sd)) 17.40 (16.13)
MS_Subtype (%)
Clinically Isolated Syndrome 2 (6.7)
Progressive-relapsing 1 (3.3)
Relapsing-remitting 24 (80.0)
Secondary-progressive 2 (6.7)
Unspecified 1 (3.3)
sex = M (%) 7 (23.3)

Imaging Data

  • 2D T1 (TR=2000ms, TE=20ms, TI=800ms) and after gadolinium
  • 2D T2 (TR=6000ms, TE=120ms), 3D FLAIR (TR=5000ms, TE=392ms, TI=1800 ms)
    • Fluid attenuated inversion recovery - reduce signal of fluids
  • All had flip angle of 120\(^{\circ}\)

OVERLAY

Terminology: Neuroimaging to Data/Statistics

  • Segmentation ⇔ classification
  • Image ⇔ 3-dimensional array
  • Mask/Region of Interest ⇔ binary (0/1) image
  • Registration ⇔ Spatial Normalization/Standarization
    • “Lining up” Brains

An Image Processing Pipeline in R

Image Representation: voxels (3D pixels)


Step 1: Create Predictors for each Sequence Preds

Data Structure for One Patient
MISTIE LOGO

Step 2: Aggregate Data

Training Data Structure

  • Stack together 15 randomly selected patients
  • Train model/classifier on this design matrix
MISTIE LOGO

Step 3: Fit Models / Classifier

Let \(y_{i}(v)\) be the presence / absence of ICH for voxel \(v\) from person \(i\).

General model form: \[ P(Y_{i}(v) = 1) \propto f(X_{i}(v)) \]

Models Fit on the Training Data

  • Logistic Regression: \(f(X_{i}(v)) = \text{expit} \left\{ \beta_0 + \sum_{k= 1}^{p} x_{i, k}(v)\beta_{k}\right\}\)
  • Random Forests (Wright and Ziegler 2017, @breiman2001random)

    \(f(X_{i}(v)) \propto\) MISTIE LOGO

Predicted Volume Estimates True Volume Reseg

Predicted Volume Estimates True Volume Reseg

Patient with Median Overlap in Validation Set

MISTIE LOGO

R Package

  • smri.process - on GitHub and Neuroconductor
    • relies on other Neuroconductor (not CRAN) packages

Conclusions of Stroke Analyses

  • We can segment ICH volume from CT scans

  • We can create population-level ICH distributions

  • Voxel-wise regression can show regions associated with severity

Conclusions of Stroke Analyses

  • We can segment ICH volume from CT scans
    • Incorporate variability of estimated volume
  • We can create population-level ICH distributions
    • Uncertainty measures of this
  • Voxel-wise regression can show regions associated with severity
    • Validate these regions (MISTIE III)
    • Scalar on image regression

Neuroimaging and R

Authored R Packages:

  • fslr

    (Muschelli, John, et al. “fslr: Connecting the FSL Software with R.” R JOURNAL 7.1 (2015): 163-175.)

  • brainR

    (Muschelli, John, Elizabeth Sweeney, and Ciprian Crainiceanu. “brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data.” R JOURNAL 6.1 (2014): 42-48.)

  • extrantsr
  • ichseg

    Muschelli, John, et al. “PItcHPERFeCT: Primary intracranial hemorrhage probability estimation using random forests on CT.” NeuroImage: Clinical 14 (2017): 379-390.

  • dcm2niir
  • matlabr
  • spm12r
  • itksnapr
  • papayar
  • WhiteStripe
  • oasis
  • SuBLIME
  • googleCite
  • diffr
  • rscopus
  • glassdoor

Number of Downloads (CRAN packages)

From the cranlogs R package:

Thank You

Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1). Springer:5–32.

Lesjak, Žiga, Alfiia Galimzianova, Aleš Koren, Matej Lukin, Franjo Pernuš, Boštjan Likar, and Žiga Špiclin. 2018. “A Novel Public MR Image Dataset of Multiple Sclerosis Patients with Lesion Segmentations Based on Multi-Rater Consensus.” Neuroinformatics 16 (1). Springer:51–63.

Wright, Marvin N., and Andreas Ziegler. 2017. “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software 77 (1):1–17. https://doi.org/10.18637/jss.v077.i01.